Abstract

It is important to discover relationships between attributes being used to predict a class attribute in supervised learning situations for two reasons. First, any such relationship will be potentially interesting to the provider of a dataset in its own right. Second, it would simplify a learning algorithm’s search space, and the related irrelevant feature and subset selection problem, if the relationships were removed from datasets ahead of learning. An algorithm to discover such relationships is presented in this paper. The algorithm is described and a surprising number of inter-attribute relationships are discovered in datasets from the University of California at Irvine (UCI) repository.